1. Field of the Invention
The present invention concerns a method for aligning an image quality characteristic of a nuclear medicine image of a patient, with the same image quality characteristic of a corresponding nuclear medicine image of the patient.
2. Description of the Prior Art
The following definitions, acronyms, and abbreviations are used herein:
NEMANational Electrical Manufacturers AssociationPETPositron Emission TomographySPECTSingle Photon Emission Computed TomographySUVStandardized Uptake Value18F-FDG18F-fluorodeoxyglucose, a radiolabelled glucose tracer
For some applications of PET (or SPECT) imaging, it is desirable to temporally subdivide an acquisition into multiple time frames to create a dynamic sequence. For example, as opposed to computing a single, average uptake value (e.g., SUV) for a given region over a duration of an acquisition, multiple measurements of uptake, such as one per frame, can be used to estimate a rate of change of uptake. This can provide an estimate of whether the rate of uptake is increasing or decreasing with time. Such an estimate of the rate of change of uptake could, for example, aid the discrimination of malignant tissue from inflammation in the case of 18F-FDG PET.
While the temporally subdivided frames permit useful analysis, a user may typically wish to review an image reconstructed from all counts acquired for a given bed position during the acquisition. This may be achieved by combining the individual reconstructed temporally subdivided frames into a single image, for example by creating an average image, weighted by frame duration. However, since fewer counts are used to reconstruct each individual frame, the count-dependent convergence behavior of the reconstruction algorithm may result in differences in image quality, for example in terms of noise, contrast recovery, between such an averaged image and an image reconstructed from all counts, despite same reconstruction algorithm and settings, for example iterations, subsets, post-filter, being used. These differences in image quality may result in higher (or lower) SUVs being measured for a given region, or different levels of visual noise in the image.
FIG. 1 shows an illustrative example of differences in image quality for an image reconstructed for all counts acquired of a NEMA Image Quality phantom as compared to an image created by averaging multiple reconstructions of the temporally subdivided frames.
Within FIG. 1, drawing (A) schematically represents a process of image reconstruction in which all acquired counts are used in the reconstruction of a single image; drawing (B) schematically illustrates the acquired counts temporally subdivided and reconstructed into a single image. The central plot illustrates a difference in contrast recovery, wherein the image A generated from a single reconstruction of all counts consistently presents improved contrast recovery over a similar image B produced by weighted averaging of the temporally subdivided data. The right-hand plot illustrates the difference in image noise, wherein the image A generated from all plots consistently presents less image noise than a similar image B produced by weighted averaging of the temporally subdivided data.
If the counts to be temporally sub-divided are acquired continuously, it is possible for them to be acquired in listmode format, allowing either a single reconstruction to be made using all counts, or alternatively as a set of reconstructions following rebinning of the data into temporal subdivisions.
If the counts are not acquired continuously, for example as for a multi-bed-position whole-body dynamic scan, this is not typically possible on current systems due to difficulties in decay correction and other limitations.